Advances in present understanding of pathophysiologic mechanisms in inflammatory bowel disease (IBD) have been enhanced enormously through genetic approaches. IBD genetics advances have been catalyzed through large collaborative efforts, notably the NIDDK IBD Genetics Consortium (NIDDK IBDGC), comprised of a central data coordinating center (DCC) and six genetics research centers (GRCs). We present preliminary data from the Immunochip project, a large international collaborative effort, which has identified 163 loci associated to IBD. Data management for Immunochip project has been managed by the NIDDK IBDGC DCC. Importantly, the identification of such a large number of new loci increases the power to integrate complementary datasets to develop predictive models that will deepen our understanding of altered biologic pathways underlying IBD susceptibility. This proposal outlines progress gene identification and network analyses. This knowledge will be critical in more accurately prioritizin which pathways to target for the development of new therapies. In Specific Aim 1, we propose to generate high quality IBD association datasets that are the foundation of a deeper understanding of disease pathogenesis. Integration of extremely large genome-wide association studies (GWAS), Immunochip data, and upcoming custom IBD exome chip data will be undertaken. Comparative studies in non-European ancestry IBD cohorts, including African-American and Puerto Rican IBD using both genome-wide and targeted approaches are proposed. Fine-mapping and annotation will refine the molecular basis for association signals. In Specific Aim 2, we provide a framework for integration genetic association data with complementary datasets in order to develop increasingly improved predictive models. Priorities for biospecimens collections and major new -omics based studies are proposed. In Specific Aim 3, we propose to enhance and expand DCC capabilities to accommodate the accelerating pace of discovery. The DCC has played an essential in successfully managing a marked increase of responsibilities and pace of discovery. This project is highly innovative due to the scope of the project and data generated, the novel integration of complementary data and cross-disciplinary, cross-institutional and cross-consortial collaborative and training efforts.